Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f4a4135b240>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f4a412380f0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(dtype=tf.float32,
                                shape=(None,image_width,image_height,image_channels),name='input_real')
    input_z = tf.placeholder(dtype=tf.float32,shape=(None,z_dim),name='input_z')
    learning_rate = tf.placeholder(dtype=tf.float32,name='learning_rate')
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator',reuse=reuse):
        alpha = 0.2
        initializer1 = tf.contrib.layers.xavier_initializer()
        x = tf.layers.conv2d(images,64,5,strides=(2,2),padding='same',kernel_initializer=initializer1)
        x = tf.maximum(alpha * x,x)
        #14*14*64
        
        initializer2 = tf.contrib.layers.xavier_initializer()
        x1 = tf.layers.conv2d(x,128,5,strides=(2,2),padding='same',kernel_initializer=initializer2)
        x1 = tf.layers.batch_normalization(x1,training=True)
        x1 = tf.maximum(alpha*x1,x1)       
        #7 * 7 * 128
        
        flat = tf.reshape(x1,shape=(-1,7*7*128))
        logits = tf.layers.dense(flat,1)
        out = tf.sigmoid(logits)
        
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [36]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('generator',reuse= not is_train):
        
        x1 = tf.layers.dense(z,7*7*512)

        x1 = tf.reshape(x1,shape=(-1,7,7,512))
        
        x1 = tf.layers.batch_normalization(x1,training=is_train)
        
        x1 = tf.maximum(alpha*x1,x1)
        
        x2 = tf.layers.conv2d_transpose(x1,128,5,strides=2,padding='same')
        
        x2 = tf.layers.batch_normalization(x2,trainable=is_train)
        
        x2 = tf.maximum(alpha*x2,x2)
        
        logits = tf.layers.conv2d_transpose(x2,out_channel_dim,5,strides=2,padding='same')
        
        out = tf.tanh(logits)
        return out




"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [37]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    model_z = generator(input_z,out_channel_dim)
    smooth = 0.1
    d_model_real , d_logits_real = discriminator(input_real,reuse=False)
    d_model_fake ,d_logits_fake = discriminator(model_z,reuse=True)
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_real,
            labels=tf.ones_like(d_model_real) * (1 - smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_fake,
            labels=tf.zeros_like(d_model_fake)))
    d_loss = d_loss_fake + d_loss_real
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_fake,
            labels=tf.ones_like(d_model_fake)))
    
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [38]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    var_list = tf.trainable_variables()
    g_var_list = [var for var in var_list if var.name.startswith('generator')]
    d_var_list = [var for var in var_list if var.name.startswith('discriminator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(d_loss,var_list=d_var_list)
        g_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(g_loss,var_list=g_var_list)
    
    return d_opt, g_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [39]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [40]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    input_real , input_z , input_learning_rate = model_inputs(data_shape[1],data_shape[2],data_shape[3],z_dim)
    channel_dim = 3 if data_shape[3] == 3 else 1
    d_loss,g_loss = model_loss(input_real,input_z,channel_dim)
    d_train_opt,g_train_opt = model_opt(d_loss,g_loss,learning_rate,beta1)
    

    steps = 0
    print_every = 100
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                num_images = len(batch_images)
                batch_z = np.random.uniform(-1,1,size=(num_images,z_dim))
                _ = sess.run(d_train_opt,feed_dict={input_real:batch_images
                                                    ,input_z:batch_z
                                                    ,input_learning_rate:learning_rate})
                _ = sess.run(g_train_opt,feed_dict={input_real:batch_images
                                                    ,input_z:batch_z
                                                    ,input_learning_rate:learning_rate})
                
                if steps%print_every == 0:
                    d_train_loss = sess.run(d_loss,feed_dict={input_real:batch_images,input_z:batch_z})
                    g_train_loss = sess.run(g_loss,feed_dict={input_real:batch_images,input_z:batch_z})
                    
                    print('Epoch is {}/{}'.format(epoch_i+1,epoch_count),
                         'Discriminator loss is {:.4f}'.format(d_train_loss),
                         'Generator loss is {:.4f}'.format(g_train_loss))
                    
                    show_generator_output(sess,num_images,input_z,channel_dim,data_image_mode)
                    
            d_train_loss = sess.run(d_loss,feed_dict={input_real:batch_images,input_z:batch_z})
            g_train_loss = sess.run(g_loss,feed_dict={input_real:batch_images,input_z:batch_z})

            print('Epoch is {}/{}'.format(epoch_i+1,epoch_count),
                 'Discriminator loss is {:.4f}'.format(d_train_loss),
                 'Generator loss is {:.4f}'.format(g_train_loss))

            show_generator_output(sess,num_images,input_z,channel_dim,data_image_mode)
                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [41]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch is 1/2 Discriminator loss is 0.4280 Generator loss is 2.7593
Epoch is 1/2 Discriminator loss is 1.4183 Generator loss is 0.6390
Epoch is 1/2 Discriminator loss is 1.0290 Generator loss is 0.8707
Epoch is 1/2 Discriminator loss is 0.6014 Generator loss is 1.8940
Epoch is 1/2 Discriminator loss is 1.5916 Generator loss is 0.4405
Epoch is 1/2 Discriminator loss is 0.4985 Generator loss is 2.3855
Epoch is 1/2 Discriminator loss is 0.6571 Generator loss is 1.5379
Epoch is 1/2 Discriminator loss is 0.4660 Generator loss is 2.4425
Epoch is 1/2 Discriminator loss is 0.8753 Generator loss is 1.5968
Epoch is 1/2 Discriminator loss is 0.7860 Generator loss is 1.6785
Epoch is 2/2 Discriminator loss is 0.6634 Generator loss is 1.7595
Epoch is 2/2 Discriminator loss is 0.4555 Generator loss is 3.4264
Epoch is 2/2 Discriminator loss is 0.4013 Generator loss is 3.8608
Epoch is 2/2 Discriminator loss is 0.7888 Generator loss is 1.3084
Epoch is 2/2 Discriminator loss is 0.6289 Generator loss is 1.5344
Epoch is 2/2 Discriminator loss is 0.8158 Generator loss is 1.2579
Epoch is 2/2 Discriminator loss is 0.8503 Generator loss is 1.2240
Epoch is 2/2 Discriminator loss is 0.4294 Generator loss is 3.0171
Epoch is 2/2 Discriminator loss is 0.5579 Generator loss is 2.2429
Epoch is 2/2 Discriminator loss is 2.4319 Generator loss is 4.9009

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [42]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch is 1/2 Discriminator loss is 0.3740 Generator loss is 3.7154
Epoch is 1/2 Discriminator loss is 2.6994 Generator loss is 11.2665
Epoch is 1/2 Discriminator loss is 1.5825 Generator loss is 0.7955
Epoch is 1/2 Discriminator loss is 1.5833 Generator loss is 0.9922
Epoch is 1/2 Discriminator loss is 1.4964 Generator loss is 0.9801
Epoch is 1/2 Discriminator loss is 1.6713 Generator loss is 0.5440
Epoch is 1/2 Discriminator loss is 1.4656 Generator loss is 0.7947
Epoch is 1/2 Discriminator loss is 1.4454 Generator loss is 0.6262
Epoch is 1/2 Discriminator loss is 1.4888 Generator loss is 0.7551
Epoch is 1/2 Discriminator loss is 1.8742 Generator loss is 0.9132
Epoch is 1/2 Discriminator loss is 1.5831 Generator loss is 0.9842
Epoch is 1/2 Discriminator loss is 1.5270 Generator loss is 0.6612
Epoch is 1/2 Discriminator loss is 1.4038 Generator loss is 0.8678
Epoch is 1/2 Discriminator loss is 1.4575 Generator loss is 0.9008
Epoch is 1/2 Discriminator loss is 1.8773 Generator loss is 0.5334
Epoch is 1/2 Discriminator loss is 1.5126 Generator loss is 0.6673
Epoch is 1/2 Discriminator loss is 1.5260 Generator loss is 0.5851
Epoch is 1/2 Discriminator loss is 1.4182 Generator loss is 0.7286
Epoch is 1/2 Discriminator loss is 1.4616 Generator loss is 0.5504
Epoch is 1/2 Discriminator loss is 1.5970 Generator loss is 0.4918
Epoch is 1/2 Discriminator loss is 1.4504 Generator loss is 0.6124
Epoch is 1/2 Discriminator loss is 1.4285 Generator loss is 0.6558
Epoch is 1/2 Discriminator loss is 1.3505 Generator loss is 0.7029
Epoch is 1/2 Discriminator loss is 1.3945 Generator loss is 0.7643
Epoch is 1/2 Discriminator loss is 1.3926 Generator loss is 0.7508
Epoch is 1/2 Discriminator loss is 1.3197 Generator loss is 0.9507
Epoch is 1/2 Discriminator loss is 1.3957 Generator loss is 0.6494
Epoch is 1/2 Discriminator loss is 1.4294 Generator loss is 0.7487
Epoch is 1/2 Discriminator loss is 1.4760 Generator loss is 1.3166
Epoch is 1/2 Discriminator loss is 1.3241 Generator loss is 0.6691
Epoch is 1/2 Discriminator loss is 1.5405 Generator loss is 0.5338
Epoch is 1/2 Discriminator loss is 1.3554 Generator loss is 0.9464
Epoch is 2/2 Discriminator loss is 1.5548 Generator loss is 0.5262
Epoch is 2/2 Discriminator loss is 1.4836 Generator loss is 1.0289
Epoch is 2/2 Discriminator loss is 1.3692 Generator loss is 0.9274
Epoch is 2/2 Discriminator loss is 1.3853 Generator loss is 0.8319
Epoch is 2/2 Discriminator loss is 1.3382 Generator loss is 0.6199
Epoch is 2/2 Discriminator loss is 1.3987 Generator loss is 0.8815
Epoch is 2/2 Discriminator loss is 1.3419 Generator loss is 0.8221
Epoch is 2/2 Discriminator loss is 1.3405 Generator loss is 0.6576
Epoch is 2/2 Discriminator loss is 1.5864 Generator loss is 1.1181
Epoch is 2/2 Discriminator loss is 1.5654 Generator loss is 1.0090
Epoch is 2/2 Discriminator loss is 1.4498 Generator loss is 0.6170
Epoch is 2/2 Discriminator loss is 1.6196 Generator loss is 0.3956
Epoch is 2/2 Discriminator loss is 1.4417 Generator loss is 1.0481
Epoch is 2/2 Discriminator loss is 2.4434 Generator loss is 1.1994
Epoch is 2/2 Discriminator loss is 1.7086 Generator loss is 0.6723
Epoch is 2/2 Discriminator loss is 1.1486 Generator loss is 1.1550
Epoch is 2/2 Discriminator loss is 1.2748 Generator loss is 0.8200
Epoch is 2/2 Discriminator loss is 1.3464 Generator loss is 0.8494
Epoch is 2/2 Discriminator loss is 1.4867 Generator loss is 0.5651
Epoch is 2/2 Discriminator loss is 1.3291 Generator loss is 0.7993
Epoch is 2/2 Discriminator loss is 1.6964 Generator loss is 1.3262
Epoch is 2/2 Discriminator loss is 1.3469 Generator loss is 0.8284
Epoch is 2/2 Discriminator loss is 1.5202 Generator loss is 0.5405
Epoch is 2/2 Discriminator loss is 1.4151 Generator loss is 0.9988
Epoch is 2/2 Discriminator loss is 1.8996 Generator loss is 0.3117
Epoch is 2/2 Discriminator loss is 1.3700 Generator loss is 0.6388
Epoch is 2/2 Discriminator loss is 1.3990 Generator loss is 1.0272
Epoch is 2/2 Discriminator loss is 1.3679 Generator loss is 0.8500
Epoch is 2/2 Discriminator loss is 1.3246 Generator loss is 0.7523
Epoch is 2/2 Discriminator loss is 1.4123 Generator loss is 0.7217
Epoch is 2/2 Discriminator loss is 1.3674 Generator loss is 0.8163
Epoch is 2/2 Discriminator loss is 1.4116 Generator loss is 0.6502
Epoch is 2/2 Discriminator loss is 1.4644 Generator loss is 0.7093

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

In [ ]: